PDF(Pubmed)
Abstract：
BACKGROUND: The low live birth rate and difficult decision-making of the in vitro fertilization (IVF) treatment regimen bring great trouble to patients and clinicians. Based on the retrospective clinical data of patients undergoing the IVF cycle, this study aims to establish classification models for predicting live birth outcome (LBO) with machine learning methods.
METHODS: The historical data of a total of 1405 patients undergoing IVF cycle were first collected and then analyzed by univariate and multivariate analysis. The statistically significant factors were identified and taken as input to build the artificial neural network (ANN) model and supporting vector machine (SVM) model for predicting the LBO. By comparing the model performance, the one with better results was selected as the final prediction model and applied in real clinical applications.
RESULTS: Univariate and multivariate analysis shows that 7 factors were closely related to the LBO (with P < 0.05): Age, ovarian sensitivity index (OSI), controlled ovarian stimulation (COS) treatment regimen, Gn starting dose, endometrial thickness on human chorionic gonadotrophin (HCG) day, Progesterone (P) value on HCG day, and embryo transfer strategy. By taking the 7 factors as input, the ANN-based and SVM-based LBO models were established, yielding good prediction performance. Compared with the ANN model, the SVM model performs much better and was selected as the final model for the LBO prediction. In real clinical applications, the proposed ANN-based LBO model can predict the LBO with good performance and recommend the embryo transfer strategy of potential good LBO.
CONCLUSIONS: The proposed model involving all essential IVF treatment factors can accurately predict LBO. It can provide objective and scientific assistance to clinicians for customizing the IVF treatment strategy like the embryo transfer strategy.
METHODS: The historical data of a total of 1405 patients undergoing IVF cycle were first collected and then analyzed by univariate and multivariate analysis. The statistically significant factors were identified and taken as input to build the artificial neural network (ANN) model and supporting vector machine (SVM) model for predicting the LBO. By comparing the model performance, the one with better results was selected as the final prediction model and applied in real clinical applications.
RESULTS: Univariate and multivariate analysis shows that 7 factors were closely related to the LBO (with P < 0.05): Age, ovarian sensitivity index (OSI), controlled ovarian stimulation (COS) treatment regimen, Gn starting dose, endometrial thickness on human chorionic gonadotrophin (HCG) day, Progesterone (P) value on HCG day, and embryo transfer strategy. By taking the 7 factors as input, the ANN-based and SVM-based LBO models were established, yielding good prediction performance. Compared with the ANN model, the SVM model performs much better and was selected as the final model for the LBO prediction. In real clinical applications, the proposed ANN-based LBO model can predict the LBO with good performance and recommend the embryo transfer strategy of potential good LBO.
CONCLUSIONS: The proposed model involving all essential IVF treatment factors can accurately predict LBO. It can provide objective and scientific assistance to clinicians for customizing the IVF treatment strategy like the embryo transfer strategy.
摘要：
背景：体外受精（IVF）治疗方案的低活产率和决策困难给患者和临床医生带来了很大的麻烦。基于IVF周期患者的回顾性临床资料,本研究旨在利用机器学习方法建立预测活产结局(LBO)的分类模型.
方法：首先收集1405例接受IVF周期的患者的历史数据，然后进行单因素和多因素分析。识别具有统计学意义的因素并将其作为输入以构建用于预测LBO的人工神经网络（ANN）模型和支持向量机（SVM）模型。通过比较模型性能，选择结果较好的模型作为最终预测模型，并应用于实际临床应用。
结果：单因素和多因素分析表明，7个因素与LBO密切相关（P<0.05）：年龄，卵巢敏感指数（OSI），控制性卵巢刺激（COS）治疗方案，Gn起始剂量,人绒毛膜促性腺激素（HCG）日子宫内膜厚度，HCG日孕酮(P)值,和胚胎移植策略。通过将这7个因素作为输入，建立了基于人工神经网络和基于SVM的LBO模型，产生良好的预测性能。与ANN模型相比，SVM模型表现更好，被选为LBO预测的最终模型。在实际的临床应用中,提出的基于ANN的LBO模型可以预测具有良好性能的LBO，并推荐潜在良好LBO的胚胎移植策略。
结论：提出的涉及所有重要IVF治疗因素的模型可以准确预测LBO。它可以为临床医生提供客观和科学的帮助，以定制IVF治疗策略，如胚胎移植策略。
方法：首先收集1405例接受IVF周期的患者的历史数据，然后进行单因素和多因素分析。识别具有统计学意义的因素并将其作为输入以构建用于预测LBO的人工神经网络（ANN）模型和支持向量机（SVM）模型。通过比较模型性能，选择结果较好的模型作为最终预测模型，并应用于实际临床应用。
结果：单因素和多因素分析表明，7个因素与LBO密切相关（P<0.05）：年龄，卵巢敏感指数（OSI），控制性卵巢刺激（COS）治疗方案，Gn起始剂量,人绒毛膜促性腺激素（HCG）日子宫内膜厚度，HCG日孕酮(P)值,和胚胎移植策略。通过将这7个因素作为输入，建立了基于人工神经网络和基于SVM的LBO模型，产生良好的预测性能。与ANN模型相比，SVM模型表现更好，被选为LBO预测的最终模型。在实际的临床应用中,提出的基于ANN的LBO模型可以预测具有良好性能的LBO，并推荐潜在良好LBO的胚胎移植策略。
结论：提出的涉及所有重要IVF治疗因素的模型可以准确预测LBO。它可以为临床医生提供客观和科学的帮助，以定制IVF治疗策略，如胚胎移植策略。
